Well sampled sites model — WSS • sparta

This function is a wrapper for siteSelection and reportingRateModel that allows users the run a well sampled sites analysis as in Roy et al (2012).

Usage

WSS(
  taxa,
  site,
  time_period,
  minL = 2,
  minTP = 3,
  species_to_include = unique(taxa),
  overdispersion = FALSE,
  family = "Binomial",
  verbose = FALSE,
  print_progress = FALSE
)

Arguments

taxa: A character vector of taxon names, as long as the number of observations.
site: A character vector of site names, as long as the number of observations.
time_period: A numeric vector of user defined time periods, or a date vector, as long as the number of observations.
minL: numeric, The minimum number of taxa recorded at a site at a given time period (list-length) for the visit to be considered well sampled.
minTP: numeric, The minimum number of time periods, or if time_period is a date the minimum number of years, a site must be sampled in for it be be considered well sampled.
species_to_include: A character vector giving the name of species to model. By default all species will be modelled
overdispersion: This option allows modelling overdispersion (TRUE) in models. Default is FALSE.
family: The type of model to be use. Can be "Binomial" or "Bernoulli".
verbose: This option, if TRUE, sets models to verbose, allowing the interations of each model to be viewed.
print_progress: Logical, if TRUE progress is printed to console when running models. Default is TRUE

Value

A dataframe of results are returned to R. Each row gives the results for a single species, with the species name given in the first column, species_name. For each of the following columns the prefix (before ".") gives the covariate and the sufix (after the ".") gives the parameter of that covariate.

number_observations gives the number of visits where the species of interest was observed. If any of the models encountered an error this will be given in the column error_message.

The data.frame has a number of attributes:

intercept_year - The year used for the intercept (i.e. the year whose value is set to 0). Setting the intercept to the median year helps to increase model stability
min_year and max_year - The earliest and latest year in the dataset (after years have been centered on intercept_year
nVisits - The total number of visits that were in the dataset
model_formula - The model used, this will vary depending on the combination of arguements used
minL - The setting of minL used in site selection
minTP - The setting of minTP used in site selection

References

Roy, H.E., Adriaens, T., Isaac, N.J.B. et al. (2012) Invasive alien predator causes rapid declines of native European ladybirds. Diversity & Distributions, 18, 717-725.

Examples

# Create data
n <- 1500 #size of dataset
nyr <- 8 # number of years in data
nSamples <- 20 # set number of dates

# Create somes dates
first <- as.POSIXct(strptime("2003/01/01", "%Y/%m/%d"))
last <- as.POSIXct(strptime(paste(2003+(nyr-1),"/12/31", sep=''), "%Y/%m/%d"))
dt <- last-first
rDates <- first + (runif(nSamples)*dt)

# taxa are set as random letters
taxa <- sample(letters, size = n, TRUE)

# three sites are visited randomly
site <- sample(c('one', 'two', 'three'), size = n, TRUE)

# the date of visit is selected at random from those created earlier
time_period <- sample(rDates, size = n, TRUE)

# combine this to a dataframe
df <- data.frame(taxa, site, time_period)

results <- WSS(df$taxa,
                df$site,
                df$time_period,
                minL = 4,
                minTP = 3,
                species_to_include = c('a', 'b', 'c'))
#> Warning: 537 out of 1500 observations will be removed as duplicates
#> boundary (singular) fit: see help('isSingular')
#> boundary (singular) fit: see help('isSingular')

# Look at the results for the first few species
head(results)
#>   species_name intercept.estimate year.estimate intercept.stderror
#> 1            a          0.3943994   -0.02898427          0.2664725
#> 2            b          0.2753493    0.01740075          0.2641437
#> 3            c          0.7243898    0.23104897          0.4574995
#>   year.stderror intercept.zvalue year.zvalue intercept.pvalue year.pvalue
#> 1     0.1057096         1.480075  -0.2741876        0.1388532  0.78394046
#> 2     0.1042904         1.042422   0.1668491        0.2972159  0.86748882
#> 3     0.1175658         1.583367   1.9652737        0.1133378  0.04938256
#>   observations
#> 1           36
#> 2           34
#> 3           38
# Look at the attributes of the object returned
attributes(results)
#> $names
#>  [1] "species_name"       "intercept.estimate" "year.estimate"     
#>  [4] "intercept.stderror" "year.stderror"      "intercept.zvalue"  
#>  [7] "year.zvalue"        "intercept.pvalue"   "year.pvalue"       
#> [10] "observations"      
#> 
#> $class
#> [1] "data.frame"
#> 
#> $row.names
#> [1] 1 2 3
#> 
#> $intercept_year
#> [1] 2007
#> 
#> $min_year
#> [1] -4
#> 
#> $max_year
#> [1] 3
#> 
#> $nVisits
#> [1] 60
#> 
#> $model_formula
#> [1] "cbind(successes, failures) ~ year + (1|site)"
#> 
#> $minL
#> [1] 4
#> 
#> $minTP
#> [1] 3
#>